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FUSIONET: A Hybrid Model Towards Image Classification

Molokwu C. Reginald

Department of Computer Science, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra state, Nigeria

E-mail Address: live.reginald@gmail.com

Corresponding author.

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Molokwu C. Bonaventure

School of Computer Science, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada

E-mail Address: molokwub@uwindsor.ca

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Molokwu C. Victor

School of Energy, Geoscience, Infrastructure, and Society, Hariot-Watt University, Edinburgh EH14 4AS, United Kingdom

E-mail Address: vcm1@hw.ac.uk

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, and

Okeke C. Ogochukwu

Department of Computer Science, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra state, Nigeria

E-mail Address: co.okeke@coou.edu.ng

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https://doi.org/10.1142/S1469026821500218Cited by:4 (Source: Crossref)

Abstract

Image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images. Contextual here means this approach is focusing on the relationship of the nearby pixels also called neighborhood. An open topic of research in computer vision is to devise an effective means of transferring human’s informal knowledge into computers, such that computers can also perceive their environment. However, the occurrence of object with respect to image representation is usually associated with various features of variation causing noise in the image representation. Hence, it tends to be very difficult to actually disentangle these abstract factors of influence from the principal object. In this paper, we have proposed a hybrid model: FUSIONET, which has been modeled for studying and extracting meaning facts from images. Our proposition combines two distinct stack of convolution operation (3 × 3 and 1 × 1, respectively). Successively, these relatively low-feature maps from the above operation are fed as input to a downstream classifier for classification of the image in question.

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